{"total":16,"rows":[{"id":"2402.04744","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","created":"2024-02-07","cited_by_count":18,"area":null},{"id":"2305.11203","title":"PDP: Parameter-free Differentiable Pruning is All You Need","created":"2023-05-18","cited_by_count":12,"area":null},{"id":"2305.12718","title":"HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity","created":"2023-05-22","cited_by_count":12,"area":null},{"id":"2302.08687","title":"VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs","created":"2023-02-17","cited_by_count":9,"area":null},{"id":"2403.07953","title":"Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2406.13868","title":"SDQ: Sparse Decomposed Quantization for LLM Inference","created":"2024-06-19","cited_by_count":4,"area":null},{"id":"2504.14365","title":"Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator","created":"2025-04-19","cited_by_count":3,"area":null},{"id":"2512.00055","title":"KAN-SAs: Efficient Acceleration of Kolmogorov-Arnold Networks on Systolic Arrays","created":"2025-11-20","cited_by_count":3,"area":null},{"id":"2409.09708","title":"ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer Acceleration","created":"2024-09-15","cited_by_count":2,"area":null},{"id":"2412.10261","title":"MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2604.18137","title":"AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2504.18628","title":"Periodic Online Testing for Sparse Systolic Tensor Arrays","created":"2025-04-25","cited_by_count":1,"area":null},{"id":"2402.17902","title":"SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2506.01166","title":"VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration","created":"2025-06-01","cited_by_count":0,"area":null},{"id":"2512.05409","title":"SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs","created":"2025-12-05","cited_by_count":0,"area":null},{"id":"2607.26515","title":"HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models","created":"2026-07-29","cited_by_count":0,"area":null}],"next_cursor":null}